if isinstance(axis, int):
axis = (axis, )
for ax in axis:
paddings[ax] = [0, 1] if reverse else [1, 0]
result = np.pad(result,
paddings,
mode='constant',
constant_values=initial_value)
slices = [slice(None)] * result.ndim
for ax in axis:
slices[ax] = slice(1, None) if reverse else slice(None, -1)
result = result[tuple(slices)]
return result
_cumprod = utils.partial(_cumop, np.cumprod, initial_value=1.)
_cumsum = utils.partial(_cumop, np.cumsum, initial_value=0.)
def _equal(x, y, name=None):
del name
x = _convert_to_tensor(x)
y = _convert_to_tensor(y)
return np.equal(x, y)
def _invert_permutation(x, name=None):
del name
x = _convert_to_tensor(x, dtype_hint=np.int32)
return np.argsort(x).astype(x.dtype)
result = _reverse(result, axis)
if exclusive:
paddings = [[0, 0]] * result.ndim
if isinstance(axis, int):
axis = (axis,)
for ax in axis:
paddings[ax] = [0, 1] if reverse else [1, 0]
result = np.pad(result, paddings, mode='constant',
constant_values=initial_value)
slices = [slice(None)] * result.ndim
for ax in axis:
slices[ax] = slice(1, None) if reverse else slice(None, -1)
result = result[tuple(slices)]
return result
_cumprod = utils.partial(_cumop, np.cumprod, initial_value=1.)
_cumsum = utils.partial(_cumop, np.cumsum, initial_value=0.)
def _equal(x, y, name=None):
del name
x = _convert_to_tensor(x)
y = _convert_to_tensor(y)
return np.equal(x, y)
def _invert_permutation(x, name=None):
del name
x = _convert_to_tensor(x, dtype_hint=np.int32)
return np.argsort(x).astype(x.dtype)